@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00236340,
 author = {韓, 秋涵 and 吉川, 厚 and 山村, 雅幸},
 book = {第86回全国大会講演論文集},
 issue = {1},
 month = {Mar},
 note = {In recent years, the ubiquitous GPS-equipped mobile devices have substantially advanced the research on personalized user mobility patterns through location data. However, accurately predicting Points of Interest (POIs) remains challenging in scenarios with sparse data. This study addresses such challenges by employing a self-supervised contrastive learning approach, which utilizes the adjacency matrices of locations within cities to predict users' subsequent POIs. This method not only improves prediction accuracy where data is limited but also sets the stage for innovative applications in deciphering human mobility and enhancing personalized services.},
 pages = {45--46},
 publisher = {情報処理学会},
 title = {Enhancing Prediction of Next Points of Interest Using Self-Supervised Contrastive Learning},
 volume = {2024},
 year = {2024}
}